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Marker assisted selection in plant breeding

Authors:
  • Faculty of Sciences- Mohammed V University of Rabat

Abstract

Marker assisted selection (MAS) is 'smart breeding' or fast track plant breeding technology. It is one tool utilized in breeding companies and research institutes for fast development of improved varieties, giving possibility to select desirable traits more directly using DNA markers. In this review, we discussed the use of MAS in biotic, abiotic, quality and other agronomic traits. Besides, we emphasized the importance of MAS at ICARDA and underlined the successful application of MAS in the last 10 years. The use of molecular markers makes the process of selecting parental lines more efficient based on genetic diversity analysis. It can aid the conventional breeding, especially for certain biotic and abiotic traits laborious to manage. Still, MAS contributed very little to the release of improved cultivars with greater tolerance to abiotic stresses, with only a few exceptions. MAS was extensively used to improve rice varieties, mainly resistant to bacterial blight and blast disease and was applied in drought tolerance along with GPC (Grain protein content) in quality traits. MAS at ICARDA is used to characterize new parental materials for disease resistance genes as well as in screening advanced lines with a focus on association mapping and identification of new QTLs. The application of MAS increased in the last decade. It is more and more used in different crops. However, rice is still the dominant crop in terms of number of publications using MAS.
237
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
Marker assisted selection in plant breeding
Fatima HENKRAR1*, Sripada UDUPA1
1 International Center for Agricul-
tural Research in the Dry Areas
(ICARDA), Rabat, Morocco
* Corresponding author
f.henkrar@cgiar.org
Received 10/07/2020
Accepted 18/07/2020
Abstract
Marker assisted selection (MAS) is ‘smart breeding’ or fast track plant breeding technol-
ogy. It is one tool utilized in breeding companies and research institutes for fast devel-
opment of improved varieties, giving possibility to select desirable traits more directly
using DNA markers. In this review, we discussed the use of MAS in biotic, abiotic,
quality and other agronomic traits. Besides, we emphasized the importance of MAS
at ICARDA and underlined the successful application of MAS in the last 10 years. e
use of molecular markers makes the process of selecting parental lines more ecient
based on genetic diversity analysis. It can aid the conventional breeding, especially for
certain biotic and abiotic traits laborious to manage. Still, MAS contributed very little
to the release of improved cultivars with greater tolerance to abiotic stresses, with only
a few exceptions. MAS was extensively used to improve rice varieties, mainly resistant
to bacterial blight and blast disease and was applied in drought tolerance along with
GPC (Grain protein content) in quality traits. MAS at ICARDA is used to characterize
new parental materials for disease resistance genes as well as in screening advanced
lines with a focus on association mapping and identication of new QTLs. e applica-
tion of MAS increased in the last decade. It is more and more used in dierent crops.
However, rice is still the dominant crop in terms of number of publications using MAS.
Keywords: marker assisted selection, plant, biotic stress, abiotic stress, quality, ICARDA
INTRODUCTION
Wheat breeders continuously seek for new techniques
which can be used for assembling target traits into
new wheat cultivars and achieve the same breeding
progress in a much shorter time than through conven-
tional breeding. e main goals of wheat breeding are
increasing the yield, improving the resistance to abiotic
and biotic stresses, improving the quality. While simple
traits can easily be detected, other complex traits such as
disease resistance or drought tolerance are much more
dicult to determine for the breeder. Young (1999)
wrote: “Before the advent of DNA marker technology,
the idea of rapidly uncovering the loci controlling com-
plex, multigenic traits seemed like a dream. Now with
DNA marker technology, this dream became reality.
e capacity of DNA markers to detect allelic variation
in the genes underlying traits oers a great promise
for plant breeding. By using DNA markers to assist in
plant breeding, eciency and precision could be greatly
increased. e use of DNA markers in plant breeding is
called marker-assisted selection (MAS).
Denition of MAS
Marker assisted selection (MAS) is ‘smart breeding’ or
fast track plant breeding technology. It is one tool utilized
in breeding companies and research Institutes for fast
development of improved varieties, giving possibility to
select desirable traits more directly using DNA mark-
ers. e molecular markers can then be used to assist
breeders track whether the specic gene or chromosome
segment(s) known to aect the phenotype of interest is
present in the individuals or populations of interest. e
potential of MAS, thus, moving from phenotype based
towards genotype based selection using markers linked
to gene of interest. anks to the advent of DNA mark-
ers in the late of 1970s, it has now become possible to
directly target genomic regions that are involved in the
expression of traits of interest.
e history of Marker assisted selection
e idea of MAS begins with the theory of quantitative
trait loci (QTLs) mapping described by sax (1923), when
he observed an association between monogenic trait
(Seed coat pigmentation) and polygenic trait (seed size).
is concept was further elaborated by oday (1961),
who suggested mapping and characterizing all QTLs
involved in complex traits using single gene marker.
e rst DNA-based genetic markers were restriction
fragment length polymorphisms, RFLPs (Botstein et
al., 1980). Permit to construct the rst map for tomato
using 57 RFLPs in 1986 (Bernatzky and Tanksley, 1986).
Beckmann and Soller (1986) described the rst use
of restriction fragment length polymorphism (RFLP)
markers in crop improvement including theoretical is-
sues related to marker-assisted backcrossing (MABC)
for improvement of qualitative traits. Tanksley et al.
(1989) published the use of RLFP as tool to select desir-
© Moroccan Journal of Agricultural Sciences • e-ISSN: 2550-553X www.techagro.org
Review
238 Henkrar and Udupa: Marker assisted selection for plant breeding
able lines. He reported the possibility to analyzing plants
at the seedling stage, screening multiple characters that
would normally be epistatic with one another, mini-
mizing linkage drag, and rapidly recovering a recurrent
parent’s genotype. At that time, the idea of selection of
target genes based on genotypes rather than phenotype
was extremely attractive to plants breeders (Young,
1999). All those initiatives open the door to marker
technology and development of simpler DNA marker
involving PCR techniques such as Random-Amplied
Polymorphic DNAs, RAPDs (Williams et al., 1990), Am-
plied Fragment Length Polymorphisms, AFLPs (Vos
et al., 1995), Simple Sequence Repeat, SSR also known
microsatellites (Powell et al., 1996) and Single Nucleo-
tide Polymorphisms, SNPs (Gupta et al., 2001). Along
with, the research boost in DNA marker technology
and produce specics markers like Sequence Character-
ized Amplied Region, SCAR (Paran and Michelmore,
1993), Cleaved Amplied Polymorphic Sequence, CAPS
(Maeda et al., 1990), Sequence Tagged Site, STS (Olsen
et al., 1989), Expressed Sequence Tags, EST (Jongeneel,
2000), and most recent marker Diversity Arrays Tech-
nology, DArT (Jaccoud et al., 2001).
e application of molecular marker in paren-
tal selection and predicting heterosis
e plant breeders seek ways of facilitating the use of
available germoplasm eectively for plant improvement.
One hand, the use of molecular markers makes the
process of selecting parental lines more ecient. Based
on genetic diversity calculated from ngerprinting data,
plant material can be classied into genetic pools. is
information can be extremely helpful for identifying
the most appropriate parental lines to be crossed. Lom-
bardi et al. (2014) reported that a selection of divergent
parental genotypes for breeding should be made active
on the basis of systematic assessment of genetic distance
between genotypes, rather than passively based on
geographical distance. In other hand, classify parental
lines into heterotic groups for the creation of predict-
able hybrids (Acquaah, 2012). e concept of heterotic
groups was developed by Maize research using RFLP-
based genetic distances of inbreds for the prediction of
hybrid performance and heterosis of single crosses in
maize has given dierent results (Melchinger, 1993). e
genetic distance estimates based on molecular marker
estimates have been eective in grouping related germ-
plasm (Melchinger et al., 1998). Martin et al. (1995)
used both pedigree records and Sequence Tagged Sites
(STS) molecular markers to determine the relationship
between genetic diversity and agronomic performance
of the hybrids and they found signicant associations
between genetic distance based on pedigree and kernel
weight and protein concentration of the heterosis. Zhao
et al. (2008) and others also suggested that genetic dis-
tances revealed by molecular markers were highly and
positively correlated with heterosis in rice. However, the
relationship between parents and genotypic variance
components in their progenies has been reported as
weak or non-signicant across many studies (Helms et
al., 1997; Burkhamer et al., 1998; Melchinger et al., 1998;
Bohn et al., 1999; Gumber et al., 1999; Brachi et al., 2010;
Hung et al., 2012).
MAS in disease resistance breeding
Plant diseases are the result of infection by other organ-
isms that adversely aect the growth, physiological func-
tioning and productivity of a plant. Plant diseases can
drastically aect a country’s economy. erefore, disease
management has always been one of the main objectives
of any crop improvement program. ere are at least
50000 diseases of economic plants and new diseases are
discovered every year (Lucas, 1992). Plant diseases are
sometimes grouped according to the symptoms they
cause (root rots, wilts, leaf spots, blights, rusts, smuts),
to the plant organ they aect (root diseases, stem dis-
eases, foliage diseases), or to the types of plants aected
(eld crop diseases, vegetable diseases, turf diseases,
etc.) (Agrios, 2004). Using plant resistance genes for
developing disease-resistant varieties are a convenient
alternative to other measures like pesticides or other
chemical control methods employed to protect crops
from diseases (Gururani et al., 2012). at is the objective
of plant breeding, the identication of resistant plants,
which are then crossed with agronomically acceptable
but susceptible plants. A program of backcrossing to the
susceptible parent and selection of resistant phenotypes
leads to the production of plants that are similar to the
susceptible parent but having the required resistance.
Breeders have successfully developed lines resistant to
diseases by integrating R-genes into their cultivars. How-
ever, it is not always the case due to the time-consuming
by conventional breeding process that take around 10
years, and by this time, in some instances, the pathogen
has already evolved a variant that is not recognized by
the improved cultivar, leading to susceptibility. DNA
markers have enormous potential to improve the ef-
ciency and precision of conventional plant breeding
via marker-assisted selection (MAS) by reducing the
reliance on laborious and fallible screening procedures.
Especially for durable resistance or no specic, that be-
comes a challenge and the best way to overcome the new
races pathogen evolution. e use of molecular markers
in selection can aid the conventional breeding, especially
for certain traits laborious to manage it. Xu and Crouch
(2008) specify four kinds of traits which DNA markers
should be helpful. (i) traits that are dicult to manage
through conventional phenotypic selection because they
are expensive or time-consuming to measure, have low
penetrance or complex inheritance; (ii) traits whose
selection depends on specic environments or host de-
velopmental stages; (iii) maintenance of recessive alleles
during backcrossing or for speeding up backcross breed-
ing in general; and (iv) pyramiding multiple monogenic
traits or several QTL for a single disease resistance with
complex inheritance. Several studies reported the ap-
plication of molecular markers as a tool to assist pheno-
typic method to improve concerned traits. For example,
Miklas et al., (2006) reported in bean that the most
eective strategy to improve bean host plant resistance
239
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
to common bacterial blight was a combination of MAS
with periodic phenotypic selection, because it allows the
retention of minor QTL and selects epistatic interactions
that contribute to improved disease resistance. Wilde et
al. (2008) noted the eciency of MAS with phenotypic
selection combination in improving resistance against
Fusarium head blight. One of the successful applications
of MAS in breeding disease resistance was in Indonisia,
and the release of two rice varieties ‘Angke’ and ‘Conde,
which are resistant to bacterial leaf blight infection
(Bustamam et al., 2002). Also, Zhao et al. (2012) succeed
in introgression of qHSR1, which is a QTL related to
head smut in head smut–susceptible lines via marker-
assisted selection, which has signicantly reduce disease
incidence over time in maize.
MAS in abiotic stress breeding
Abiotic stress is dened as environmental conditions
that reduce growth and yield below optimum levels.
Plant responses to abiotic stresses are dynamic and
extremely complex (Cramer, 2010; reviewed by Cramer
et al., 2011). Boyer (1982) indicated that environmental
factors may limit crop production by as much as 70%.
Many genes aect stress tolerance, but few of the identi-
ed genes have proven useful in the eld. e genom-
ics era has allowed dissection of the physiological and
molecular traits underlying stress tolerance mechanisms
to an unprecedented level. Integrated omics analyses
have markedly increased our understanding of plant re-
sponses to various stresses. ese analyses are important
for comprehensive analyses of abiotic stress responses,
especially the nal steps of stress signal transduction
pathways (Cramer et al., 2011). e application of
omics technologies has contributed to the development
of stress-tolerant crops in the eld. Several genes are
identied to have a great role in abiotic stress tolerance.
For instance, SNACs were characterized as factors that
regulate expression of genes important for drought and
salinity tolerance in rice (Hu et al., 2006; reviewed by
Todaka et al., 2012). DREB1/CBF regulon involved in
cold-stress-responsive gene expression, and DREB2
involved in osmotic-stress-responsive gene expression
(Yamaguchi-Shinozaki and Shinozaki, 2006). e re-
views of Nakashima et al. (2009) and Todaka et al. (2012)
discussed more about dierent abiotic stress genes iden-
tied in transcriptomic analyses. is comprehensive
knowledge about the genes involved in stress response
and tolerance will further allow a more precise use of
MAS and transgenics (Dita et al., 2006). However, still
MAS contributed very little to the release of improved
cultivars with greater tolerance to abiotic stresses, with
only a few exceptions (LeDeaux et al., 2006; MacMillan
et al., 2006; Ribaut and Ragot, 2007; Welcker et al., 2007).
e marker assisted selection was applied especially in
drought tolerance. For instance, Courtois et al. (2003)
used MAS to transfer a number of QTLs related to a
deep rooted character from the japonica upland cultivar
‘‘Azucena’’ to the lowland indica variety ‘‘IR64’. MAS se-
lected lines showed a greater root mass and higher yield
in drought stress. Steele et al. (2004) made novel method
termed Marker-evaluation selection in rice crop. is ap-
proach used a very large segregating population derived
from a wide cross between the upland variety Kalinga
III and the irrigated variety IR64. e population was
selected for overall agronomic performance in several
target stress environments over many generations and
the products from the selection were evaluated with
markers. Varieties developed through MABC (e.g. Asho-
ka 228) have better drought resistance as they yield more
than parent Kalinga III. Similarly, Steele et al. (2006)
used marker assisted breeding program to improve some
root traits related to drought tolerance in an Indian rice
cultivar Kalinga III. ey introgressed ve QTL regions
associated with root traits from Azucena into Kalinga
III. e target QTL on chromosome 9 (RM242-RM201)
signicantly increased root lengths under drought stress.
MAS in improving agronomic and seed quality
traits
Development of cultivars with high agronomic perfor-
mance and good quality is preeminent in crop breeding
programs. Several agronomic and quality traits are poly-
genic trait controlled by many QTL/genes with smaller
eects, such as yield and GPC, seed size seed oil content,
days to ower and to maturity, ber length and strength,
etc.; or by few QTL/genes with major eects such as ker-
nel color, ower color, stem color, etc. ose traits cannot
be found through phenotypic evaluation alone because
they are highly sensitive to environmental changes. In
addition, it is dicult to produce ideal cultivars with high
yield and good quality due to the existing negative corre-
lation between those traits (Barnard et al., 2002; Chung et
al., 2003; Yagdi and Sozen, 2009; Sourour et al., 2018; Ma
et al., 2012). erefore, Molecular detection and genetic
tracking of quantitative trait loci (QTL) for agronomic
and quality traits will aect positively in manipulation
of those traits, and will increase the accuracy of selec-
tion. Hence, the identication of QTLs related to quality
and agronomic traits is important as an entry point for
marker assisted selection. Nowadays, the studies are fo-
cusing on desiccation of stable QTLs responsible for ag-
ronomic and quality traits in major crops using genome
wide association mapping (GWAS), linkage mapping
and single nucleotide polymorphism (SNPs). Chen et al.
(2016) identies useful QTL qGW4.05 related to Kernel
weight and kernel size in Maize. e agronomic and
quality traits of Brassica napus has been dissected using
Genome wide association mapping and using a 6K single
nucleotide polymorphism (SNP) array (Körber et al.,
2016). New QTLs associated with protein and oil content
were identied (Cao et al., 2017; Karikari et al., 2019).
e MAS was extensively used for improving GPC. e
selection and introgression of a high GPC allele of Gpc-
B1 has been achieved in several of the released wheat
cultivars (DePauw et al., 2005; Humphreys et al., 2010;
Randhawa et al., 2013) using molecular markers. A suc-
cessful example of an integrated approach of combining
phenotypic selection with marker assisted backcross
breeding in wheat for introgression of Gpc-B1 in Indian
240 Henkrar and Udupa: Marker assisted selection for plant breeding
wheat cultivar HUW468 (Vishwakarma et al., 2016).
MAS was adopted for studying the genome composition
of winter cultivars Zhengmai 7698 using closely linked
or functional markers for gluten protein quality, grain
hardness and our color (Li et al., 2018). It was used to
improve oil content in sunower, and the Marker F4-
R1 was validated and proved to be the most ecient in
detecting high oil content in sunower (Dimitrijević et
al., 2017). Besides, e MAS was frequently used in the
most important trait, yield. Liang et al. (2004) developed
a new stable improved line ‘9311xOryza rupogon’
with yield-enhancing genes and high yield potential
using SSRs tightly linked markers. Kumar et al. (2018)
combined grain yield and genotypic data from dierent
generations (F3 to F8) for ve marker-assisted breeding
programs for analyzing the eectiveness of synergistic ef-
fect of phenotyping and genotyping in early generations.
ey found genotyping and phenotyping cost savings of
25–68% compared with the traditional marker-assisted
selection approach.
Marker assisted selection at ICARDA
Crop improvement at ICARDA aims to conserve agricul-
tural biodiversity in dry areas and to use these resources
to improve food crops through breeding. It covers durum
and bread wheat, barley, chickpea, lentil, faba bean, gras-
spea, and forage and pasture crops. ICARDAs approach
combines conventional and biotechnology research to
identify molecular markers and to use it. Identication
and utilization of molecular markers for marker assisted
selection would enhance the development of widely
adapted and high yielding varieties with resistance/toler-
ance to abiotic and biotic resistance and acceptable level
of end use quality. e benet of this ‘marker-assisted
selection’ is that it will make the breeding process faster
and more precise. As a result, breeders and farmers will
see rapid improvements in crop production, enabling
them to improve livelihoods and boost food security.
MAS at ICARDA is used to characterize new parental
materials for disease resistance genes (stripe rust, leaf
rust, stem rust, nematodes); insect resistance (Hessian y
and Russian Wheat Aphid), phonological traits such as
photoperiodism (Ppd), vernalization requirement (Vrn);
plant height (Rht), grain hardness and other desirable
genes (Tadesse et al., 2012 and 2016). Molecular markers
are also used for pyramiding dierent resistance genes
and developing multi-line cultivars targeting for durable
resistance to the disease. It helps of screening real hybrids
F1, F2, BC1F1 populations. e use of molecular markers
and MAS started at ICARDA since long, by identifying
and mapping gene resistance to lentil, pea and chickpea
pathogen (Baum et al., 2000). e use of molecular
techniques and biotechnology tools have expanded
considerably, the techniques are applied to almost all
crops and concentrated on the development of marker-
assisted selection and characterization and identica-
tion of fungal pathogens and nematodes. ICARDA has
focused on the propagation of the molecular techniques
and their application in crop improvement by organiz-
ing extensive training to young researchers, students,
junior level scientists, and also technicians (Ryan et al.,
2012). CIMMYT, Biodiversity, International Centre for
Agricultural Research in the Dry Areas (ICARDA), and
IRRI have partnered with national research organizations
from 13 countries in Africa and South Asia to co-generate
and share technologies for genetic characterization and
marker-assisted improvement of wheat, barley, and rice,
focusing on traits and alleles that are important for the
crops adaptation to climatic changes (Halewood et al.,
2018). Several works done by ICARDA scientists and
students on MAS were published. (Halewood et al., 2018)
discriminates between resistant and susceptible chickpea
genotypes using two codominant markers associated
to Ascochyta blight. Molecular marker associated with
grain yield under drought conditions such as the CID,
are actively and eectively used in the ongoing breeding
program (Nachit, 1998; Nachit and Eloua, 2004). Dura
et al. (2012) identied potential targets for MAS of grain
yield improvement in durum wheat in ICARDA labora-
tory. Recently, the markers assisted selection has been
successfully used to enhance tolerance against Barley
scald (Sayed & Baum, 2018). Nowadays, ICARDA is fo-
cusing on Association mapping (AM) using phenotypic
and genotypic data of association panels, due to the im-
portance of this approach in identifying molecular mark-
ers (QTLs) linked to traits of interest for potential use in
marker assisted selection. In barley, association mapping
was undertaken to identify QTL eective against Psh in-
dividual races at seedling stage and QTL for quantitative
resistance to barley stripe rust at seedling and adult plant
stages (Visioni et al., 2018). In wheat, genome-wide as-
sociation mapping (GWAM) was employed using DArT
markers technology and ICARDAs elite wheat genotypes
to identify markers linked to stripe rust resistance genes
in wheat for possible use in MAS (Tadesse et al., 2014;
Jighly et al., 2015) employed genome-wide association
mapping (GWAM). In pulse, the association mapping
was designed to determine the genetic basis of seed Fe
and Zn concentration in lentil by using single-nucleotide
polymorphism (SNP) array derived from cultivated lentil
sequences (Singh et al., 2017).
Successful application of MAS in last decade
e MAS of smart breeding method is the method of
choice for all breeders. It has been implemented in dif-
ferent crop programs. Several publications declare the
application of MAS in crop improvement. But still the
number of successful application of this method is less
compared to the number of QTLs mapped or markers
developed. Moreover, most marker associations are not
robust enough for successful marker assisted selection
(Young et al., 1999). By using Harzing’s Publsih or Perish
soware (Harzing, 2007) and using the query ‘Marker as-
sisted selection’ in Google scholar and in Scopus between
2010 and 2019, around 571 publications were retrieved
in which the title included ‘Marker assisted’. At rst sight
it was oen dicult to distinguish from the title whether
a publication is actually reporting a MAS application or
if only potential MAS applications of the actual research
outputs are discussed. erefore, the publications were
241
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
selected by reading the abstracts and sometime the ma-
terial and methods to distinguish the real application of
MAS. e results mentioned in table 1 is the number
of publications harvested using MAS keyword. Among
571, only 189 publications were the real applications
of MAS. Whereas, others publications were reviews of
MAS (163 publications), QTL mapping or identica-
tion and/or marker development and validation (149
publications), Characterization and genetic diversity
(47 publications) or genomic selection (23 publications).
e MAS practical publications were dominant in rice
with 87 publications (Figure 1), 29 of them are on bacte-
rial blight diseases. e number of publications in other
cereals was limited to 39 publications (18 publications
in wheat, 18 publications in Maize and 3 publications in
Barley). Marker assisted backcrossing (100 publications,
Figure 2) has been most widely and successfully used
up-to-date, compared to other methods such as pedigree
method (40 publications), pyramiding (45 publications)
and MARS (4 publications). It has been applied to dif-
ferent crops, e.g. rice, wheat, maize, barley, pear millet,
soybean, tomato, etc.
Table 1: Total number and type of publications re-
trieved from Harzing’s Publish from 2010 to 2019
(Harzing, 2007)
Type of publications Number of publica-
tions
MAS articles 189
Reviews 163
Characterization or genetic diversity 47
Mapping or marker development 149
Genomic selection 23
Tota l 571
Figure 1: Number of MAS publications applied to dierent crops in the last 10 years
Figure 2: Number of publications for dierent types of MAS collected during the last 10 years
242 Henkrar and Udupa: Marker assisted selection for plant breeding
Table 2: Examples of successful use of marker assisted selection in dierent crops for the last 5 years
Target trait Gene (s)/QTl(s)
Type of
Marker
used
Name of marker used Crop Reference
Blast Pi2 STS Pi2–4 , HC28 Rice Yang et al., 2019
Bacterial blight and
aroma
Xa21, xa13, xa5,
fgr STS pTA248, RG136, RG556, BAD2 Rice Baliyan et al.,
2018
Quality protein Opaque2 (o2) SSR umc1066 and phi057 Maize
Hossain et al.,
2018; Pukalen-
thy et al., 2019
Scald Rrs1 SSR/
SCAR
Ebmac0871-
SSR, HVS3-SCAR, Bmag0006-
SSR
Barley Sayed and
Baum, 2018
HMW, Grain hardness,
Lipoxygenase,Yellow
pigment content,
Polyphenol oxidase,
Powdery mildew, Yel-
low rust, Pre-harvest
sprouting
SSR/STS,
allele
specic
UMN19, Bx7, ZSBy8, ZSBy9a,
UMN25, Dx5, UMN26, Pinb-D1a,
LOX16, LOX18, YP7A, YP7B-1,
YP7D-1, PPO18, PPO19, PPO29,
Pm2, Pm4b, Pm8, Xgwm582,
Xcfa2040, PHS1, PHS-4AL
Wheat Li et al., 2018
Blast Pi54, Pi1 and Pita STS, SSR Pi54MAS, RM224, YL155/87 Rice Khan et al., 2018
Bacterial blight Xa38, Xa21,
Xa13 and Xa5
Gene
specic
markers/
STS
Os04g53050-1, pTA248, xa13-
Prom, 10603-T10Dw Rice Yugander et al.,
2018
Bacterial blight Gm1, Gm4,
xa13 and Xa21 SSR RM1328, RM22550, xa13 prom and
pTA248 Rice
Krishnakumar
and Kumaravadi-
vel 2018
Mosaic virus RSC4, RSC8, and
RSC14Q SSR
BARCSOYSSR_14_1413, 4
BARCSOYSSR_14_1417,
BARCSOYSSR_14_1418,
BARCSOYSSR_02_0606,
BARCSOYSSR_02_0610, BARC-
SOYSSR_02_0616, BARCSOYS-
SR_02_0618, Satt334, Sct_033,
MY750
Soybean Wang et al.,
2017
Rust and coee berry SH3, SH?, Ck-1 SCAR/
SSR
SP-M16-SH3, BA-124-12K-f,
Sat244, BA-48-21OR, CaRHvII 2,
CaRHvII 3, CaRHvII 5, Sat 207,
Sat 235
Coea Alkimim et al.,
2017
Striga SG1, SG3, and
SG5 SSR 61RM2, SSR-1 and C42-2B Cowpea OMOIGUI et al.,
2017
Rust Lr19 and Lr24 SCAR/
SSR Xwmc221 and SCS1302 Wheat Singh et al.,
2017
Rust Lr24 and Lr28 SCAR/
SSR
SCS719, SCS1302607, SCS421570
and Xwmc313 Wheat Kumar et al.,
2017
Drought, Striga her-
monthica SNPs KASP
markers 233 SNPs with KASP assay Maize Abdulmalik et
al., 2017
Bacterial blight, Blast Xa21 and xa13,
Pi54 STS xa13 prom, pTA 248 and Pi54 MAS Rice Arunakumari et
al., 2016
Quality protein opaque2 SSR phi057 and umc1066 Maize Kostadinovic et
al., 2016
Grain protein con-
tent, Thousand grain
weight
GPC-B1 and TGW SSR Xucw108, Xgwm297 Wheat
Vishwakarma
et al., 2016 and
2014
Fusarium head blight Fhb7, Fhb1 SSR XsdauK66 and Xcfa2240 (Fhb7),
Xgwm493 and Xgwm533 (Fhb1) Wheat Guo et al. 2015
Leaf curl disease Ty-2, Ty-3, Ty-5 Linked
markers Ty-2, Ty-3, Ty-5, qTy10.1 Tomato Prasanna et al.,
2015
Blast and bacterial
blight Pi9(t), Xa23, tms5
SCAR/
EST/Indel
marker
Pb8, C189, IDtms5 Rice Ni et al., 2015
Rice tungro disease RTSV SSR RM336 Rice Shim et al., 2015
243
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
e successful publications using MAS in last 5 years is
resumed in table 2. One successful example of marker
assisted backcrossing and pyramiding is the introgres-
sion of three BB resistance genes (Xa21, xa13 and xa5)
from BB-resistant donor variety IRBB-60 into the BB-
susceptible Basmati variety CSR-30 (Baliyan et al., 2018).
A successful introgression of Shoot Fly (Atherigona soc-
cataL. Moench) Resistance QTLs into Elite Post-rainy
Season Sorghum varieties (Gorthy et al., 2017). An
example of a successful application of MAS in breed-
ing new cultivars is the development of “Mura Salad
a new fresh pepper cultivar (Capsicum annuum) con-
taining capsinoids, low-pungent capsaicinoid analogs
using dCAPs and SCAR markers (Tanaka et al., 2014).
In legumes, a successful application of marker assisted
backcrossing in chickpea and specic markers for Fu-
sarium wilt-resistance generate the development of new
cultivars Super Annigeri 1 and improved JG 74 with
enhanced resistance and improved yielding (Mannur
et al., 2019). MAS were successfully applied in wheat to
improve GPC-B1 (84-60) and also in Barley to transfer
a thermostable β-amylase gene (Xu et al., 2018) scald
(Rhynchosporium commune L.) resistance gene (Sayed
and Baum, 2018).
CONCLUSION
Marker assisted selection is a technology that has already
proved its value. Due to the number of QTLs, genes and
markers identied the MAS is likely to become more
valuable. Many organizations and private sectors suc-
ceed in implementing MAS and produced new lines
with desirable traits. But still reduced cost and optimized
strategies for integrating MAS with phenotypic selec-
tion are needed before the technology can reach its full
potential.
REFERENCES
Abdulmalik R., Menkir A., Meseka S.K., Unachukwu N.,
Ado S.G., Olarewaju J.D., Aba D.A., Hearne S., Crossa J.,
Gedil M. (2017). Genetic gains in grain yield of a maize
population improved through marker assisted recurrent
selection under stress and non-stress conditions in West
Africa.Front. Plant Sci., 8: 841.
Acquaah G. (2012). Principles of plant genetics and
breeding. 2nd ed. Wiley-Blackwell, Oxford.
Agrios G.N., (2004). Plant pathology.5th ed. London:
Elsevier, 922 p.
Alkimim E.R., Caixeta E.T., Sousa T.V., Pereira A.A., de
Oliveira A.C.B., Zambolim L., Sakiyama N.S. (2017).
Mol Breeding, 37: 6.
Arunakumari K., Durgarani C.V., Satturu V., Sarikonda
K.R., Chittoor P.D.R., Vutukuri B., Laha G.S., Nelli
A.P.K., Gattu S., Jamal M., Prasadbabu A., Hajira S.,
Sundaram R.M. (2016). Marker-Assisted Pyramiding
of Genes Conferring Resistance Against Bacterial Blight
and Blast Diseases into Indian Rice Variety MTU1010.
Rice Sci., 23: 306−316.
Baliyan N., Malik R., Rani R., Mehta K., Vashisth U.,
Dhillon S., Boora K.S. (2018). Integrating marker-
assisted background analysis with foreground selection
for pyramiding bacterial blight resistance genes into
Basmati rice. C. R. Biol., 341: 1-8.
Barnard A.D., Labuschagne M.T., Van Niekerk H.A.
(2002). Heritability estimates of bread wheat quality
traits in e Western Cape Province of South Africa.
Euphytica, 127: 115‒122.
Baum M., Weeden N.F., Muehlbauer F.J., Kahl G., Udupa
S.M., Eujay I., Weigand F., Harrabi M., Bouznad Z.
(2000). Marker technology for plant breeding. In: Knight
R. (eds) Linking Research and Marketing Opportunities
for Pulses in the 21st Century. Curr. Plant Sci. Biot. vol
34. Springer, Dordrecht.
Beckmann J.S., Soller M. (1986). Restriction fragment
length polymorphisms and genetic improvement of
agricultural species, Euphytica, 35: 111-124.
Bernatzky R., Tanksley S.D. (1986). Towards a saturated
linkage map in tomatoes based on isozyme and random
cDNA sequences.Genetics,112: 887–898.
Bohn M., Utz H.F., Melchinger A.E. (1999). Genetic
similarities among winter wheat cultivars determined
on the basis of RFLPs, AFLPs, and SSRs and their use
for predicting progeny variance.Crop Sci.,39:228–237.
Botstein D., White R.L., Skolnick M., Davis R.W. (1980).
Construction of a genetic linkage map in man using re-
striction fragment length polymorphisms. Am. J. Hum.
Genet., 32: 314- 331.
Bouhadida M., Benjannet R., Madrid E., Amri M.,
Kharrat M. (2013). Eciency of marker-assisted selec-
tion in detection of Ascochyta blight resistance in Tu-
nisian chickpea breeding lines. Phytopathol. Mediterr.,
52: 202-211.
Boyer J.S. (1982). Plant productivity and environ-
ment.Science, 218:443–448.
Brachi B., Faure N., Horton M., Flahauw E., Vazquez A.,
Nordborg M. (2010). Linkage and association mapping
of Arabidopsis thaliana owering time in nature.PLoS
Genet.,6:e1000940.
Burkhamer R.L., Lanning S.P., Martens R.J., Martin
J.M., Talbert L.E. (1998). Predicting progeny variance
from parental divergence in hard red spring wheat.Crop
Sci.,38:243–248.
Bustamam M., Tabien R.E., Suwarno A., Abalos M.C.,
Kadir T.S., Ona I., B ernardo M., Veracruz C.M., Leung H.
(2002). Asian Rice Biotechnology Network: Improving
Popular Cultivars rough Marker-Assisted Backcross-
ing by the NARES. Poster presented at the International
Rice Congress. September 16-20, Beijing, China.
Cao Y., Li S., Wang Z., Chang F., Kong J., Gai J., Zhao T.,
(2017). Identication of Major Quantitative Trait Loci
for Seed Oil Content in Soybeans by Combining Linkage
and Genome-Wide Association Mapping. Front Plant
Sci.,8: 1222.
Chen L., Li Y.X., Li C., Wu X., Qin W., Li X., Wang T.
(2016). Fine-mapping of qGW4.05, a major QTL for
kernel weight and size in maize.BMC Plant Biol.,16: 81.
244 Henkrar and Udupa: Marker assisted selection for plant breeding
Chung O.K., Ohm J.B., Lookhart G.L. (2003). Quality
characteristics of hard winter and spring wheats grown
under an Over‒Wintering Condition. J. of Cereal Sci.,
37:91‒99.
Courtois G., Smahi A., Reichenbac J., Dönger R.,
Cancrini C., Bonnet M., Casanova J.L. (2003). A hyper-
morphic IkappaBalpha mutation is associated with au-
tosomal dominant anhidrotic ectodermal dysplasia and
T cell immunodeciency.J. Clin. Invest.,112: 1108–1115.
Cramer G.R. (2010). Abiotic stress and plant responses
from the whole vine to the genes. Aust. J. Grape Wine
Res., 16: 86-93.
Cramer G.R., Urano K., Delrot S., Pezzotti M., Shinozaki
K. (2011). Eects of abiotic stress on plants: a systems
biology perspective.BMC Plant Biol.,11:163.
DePauw R.M., Townley-Smith T.F., Humphreys G.,
Knox R.E., Clarke F.R., Clarke J.M. (2005). Lillian hard
red spring wheat. Can. J. Plant Sci., 85: 397-401.
Dimitrijević A., Ivana I., Dragana M., Sandra C., Siniša
J., Tijana Z., Zvonimir S. (2017). Oleic acid variation
and marker-assisted detection of Pervenets mutation in
high- and low-oleic sunower cross. Crop Breed. Appl.
Biot., 17: 235-241.
Dita M.A., Rispail N., Prats E., Rubiales D., Singh K.B.
(2006). Biotechnology approaches to overcome biotic
and abiotic stress constraints in legumes. Euphytica,
147:1–24.
Dura S., Mahmud A.M., Duwayri A., Miloudi M.N.
(2012). Detection of molecular markers associated with
yield and yield components in durum wheat (Triticum
turgidum L. var. durum Desf.) under drought conditions.
African Journal of Agricultural Research, 8:2118-2128.
Gorthy S., Narasu L., Gaddameedi A., Sharma H.C.,
Kotla A., Deshpande S.P., Are A.K. (2017). Introgression
of Shoot Fly (Atherigona soccataL. Moench) Resistance
QTLs into Elite Post-rainy Season Sorghum Varieties
Using Marker Assisted Backcrossing (MABC). Front
Plant Sci.,8: 1494.
Gumber R.K., Schill B., Link W., von Kittlitz E., Melch-
inger A.E. (1999). Mean, genetic variance, and usefulness
of selng progenies from intra- and inter-pool crosses
in faba beans (Vicia faba L.) and their prediction from
parental parameters.eor. Appl. Genet.,98:569–580.
Guo J., Zhang X., Hou Y,. Cai J., Shen X., Zhou T., Xu H.,
Ohm H.W., Wang H., Li A., Han F., Wang H., Kong L.
(2015). High-density mapping of the major FHB resis-
tance gene Fhb7 derived from inopyrum ponticum an d
its pyramiding with Fhb1 by marker-assisted selection.
eor. Appl. Genet., 128: 2301-16.
Gupta S., Srinagesh D., Rai S.S., Gaur V.K., Priestley K.,
Du Z. (2001). e thick early Archean crust-Teleseismic
results from Dharwar Craton, Eos Trans. AGU, 82(47),
Fall Meet. Suppl., S11D-03.
Gururani M.A., Upadhyaya C.P., Strasser R.J., Woong
Y.J., Park S.W. (2012. Physiological and biochemical re-
sponses of transgenic potato plants with altered expres-
sion of PSII manganese stabilizing protein Plant Physiol.
Biochem., 58: 182-194.
Halewood M., Chiurugwi T., Hamilton R.S., Kurtz B.,
Marden E., Welch E., Michiels F., Mozafari J., Sabran
M., Patron N., Kersey P., Bastow R., Dorius S., Dias S.,
McCouch S., Powell W. (2018).Plant genetic resources
for food and agriculture: opportunities and challenges
emerging from the science and information technology
revolution.New Phyto.,217: 1407-1419.
Harzing A.W. (2007). Publish or Perish, available from
https://harzing.com/resources/publish-or-perish
Helms T., Orf J., Vallad G., McClean P. (1997). Genetic
variance, coecient of parentage, and genetic distance of
six soybean populations.eor. Appl. Genet.,94:20–26.
Hossain F., Muthusamy V., Pandey N., Vishwakarma A.
K., Baveja A., Zunjare R. U., Gupta, H. S. (2018). Marker-
assisted introgression of opaque2 allele for rapid conver-
sion of elite hybrids into quality protein maize. Journal
of genetics, 97: 287-298.
Hu H., Dai M., Yao J., Xiao B., Li X., Zhang Q., Xiong L.
(2006).Overexpressing a NAM, ATAF, and CUC (NAC)
transcription factor enhances drought resistance and salt
tolerance in rice.Proc. Natl. Acad. Sci.,103: 12987-12992.
Humphreys D.G., Townley-Smith T.F., Lukow O., Mc-
Callum B., Gaudet D,. Gilbert J., Fetch T., Menzies J.,
Brown D., Czarnecki E. (2010). Burnside extra strong
hard red spring wheat. Can. J. Plant Sci., 90: 79-84.
Hung H.Y., Browne C., Guill K., Coles N., Eller M.,
Garcia A., Lepak N., Melia-Hancock S., Oropeza-Rosas
M., Salvo S., Upadyayula N., Buckler E.S., Flint-Garcia
S., McMullen M.D., Rocheford T.R., Holland J.B. (2012).
e relationship between parental genetic or phenotypic
divergence and progeny variation in the maize nested as-
sociation mapping population.Heredity,108: 490–499.
Jaccoud D., Peng K., Feinstein D., Kilian A. (2001).
Diversity arrays: a solid state technology for sequence
information independent genotyping. Nucleic Acids
Research, 29, e25.
Jighly A., Oyiga B.C., Makdis F., Nazari K., Youssef O.,
Tadesse W., Abdalla O., Ogbonnaya F.C. (2015). Ge-
nome-wide DArT and SNP scan for QTL associated with
resistance to stripe rust (Puccinia striiformis f. sp. tritici) in
elite ICARDA wheat (Triticum aestivum L.) germplasm.
eor. Appl. Genet., 128:1–19.
Jongeneel C.V. (2000). Searching the expressed sequence
tag (EST) databases: panning for genes. Briengs in Bio-
informatics, 1:76-92.
Karikari B., Li S., Bhat J.A., Cao Y., Kong J,. Yang J., Gai J.,
Zhao T. (2019). Genome-Wide Detection of Major and
Epistatic Eect QTLs for Seed Protein and Oil Content
in Soybean Under Multiple Environments Using High-
Density Bin Map.Int. J. Mol. Sci.,20: 979.
Khan G.H., Shikari A.B., Vaishnavi R., Najeeb S., Pad-
der B.A., Bhat Z.A., Parray G.A., Bhat M.A., Kumar R.,
Singh N.K. (2018). Marker-assisted introgression of
three dominant blast resistance genes into an aromatic
rice cultivar Mushk Budji.Sci. Rep., 8:4091.
Körber N., Bus A., Li J., Parkin I.A., Wittkop B., Snowdon
R.J., Stich B. (2016). Agronomic and Seed Quality Traits
Dissected by Genome-Wide Association Mapping in
Brassica napus.Front. Plant Sci.,7, 386.
245
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
Kostadinovic M., Ignjatovic-Micic D., Vancetovic J.,
Ristic D., Bozinovic S., Stankovic G., Mladenovic Drinic
S. (2016). Development of High Tryptophan Maize Near
Isogenic Lines Adapted to Temperate Regions through
Marker Assisted Selection - Impediments and Ben-
ets.PLoS One 11: e0167635.
Krishnakumar R., Kumaravadivel N. (2018). Marker-
assisted selection for biotic stress (Bacterial leaf blight
and gall midge) tolerance in Bc4F4 generation of rice
(Oryza sativa L.). Plant Breeding, 9: 275 – 282.
Kumar A., Sandhu N., Dixit S., Yadav S., Swamy B.,
Shamsudin N. (2018). Marker-assisted selection strat-
egy to pyramid two or more QTLs for quantitative trait-
grain yield under drought.Rice (New York, N.Y.).11: 35.
Kumar G., Anil R.R., Hanchinal Shrinivas D., Suma B.
(2017). Marker Assisted Gene Pyramiding of Leaf Rust
Resistance Genes Lr24 and Lr28 in the Background of
Wheat Variety DWR 162 (Triticum aestivum L.). Int. J.
Curr. Microbiol. App. Sci., 6: 1883-1893.
LeDeaux J.R., Graham G.I., Stuber C.W. (2006). Stability
of QTLs involved in heterosis in maize when mapped
under several stress conditions.Maydica,51:151–167.
Li C.X., Xu W.G., Guo R., Zhang J.Z., Qi X.L., Hu L.,
Zhao M.Z. (2018). Molecular marker assisted breeding
and genome composition analysis of Zhengmai 7698,
an elite winter wheat cultivar.Scientic reports,8: 322.
Li L., Tacke E., Hoerbert H-R., Lubeck J., Strahwald J.,
Draehn A.M., Walkemeier B., Gebhardt C. (2013). Vali-
dation of candidate gene markers for marker-assisted
selection of potato cultivars with improved tuber quality.
eor. Appl. Genet., 126:1039–1052.
Liang F., Deng Q., Wang Y., Xiong Y., Jin D., Li J., Wang
B. (2004). Molecular marker-assisted selection for yield-
enhancing genes in the progeny of “9311×O. rupogon
using SSR. Euphytica, 139: 159–165.
Lombardi M., Materne M., Cogan N.O.I., Rodda M.,
Daetwyler H.D., Slater A.T., Forster J.W,. Kaur S. (2014).
Assessment of genetic variation within a global collec-
tion of lentil (Lens culinarisMedik.) cultivars and land-
races using SNP markers. BMC Genet., 15:150.
Lucas P.M. (1992). Health and Disease in the Late Pre-
historic Southeast in Verano, J.W. & Ubelaker, D.H. (eds)
Disease and Demography in the Americas, Smithsonian
Institute Press, Washington.
Ma J., Zhang H., Li S., Zou Y., Li T., Liu J., Ding P., Mu
Y., Tang H., Deng M., Liu Y., Jiang Q., Chen G., Kang H.,
Li W., Pu Z., Wei Y., Zheng Y., Lan X. (2019). Identica-
tion of quantitative trait loci for kernel traits in a wheat
cultivar Chuannong16. BMC Genet., 20:77.
MacMillan K., Emrich K., Piepho H.P., Mullins C.E.,
Price AH. (2006). Assessing the importance of genotype
× environment interaction for root traits in rice using a
mapping population. I: A soil-lled box screen.eor.
Appl. Genet.,113: 977–986. 
Maeda Y., Yamanaka Y., Sasaki A., Suzuk M., Ohtaishi
N. (1990). Haematology in sika deer (Cervus nippon ye-
soensis Heude, 1884). e Japanese Journal of Veterinary
Science, 52: 35-41.
Mannur D.M., Babbar. A, udi M., Sabbavarapu M.M.,
Roorkiwal M., Yeri S.B., Bansal V.P., Jayalakshmi S.K.,
Yadav S.S., Rathore A., Chamarthi S.K., Mallikarjuna
P.B., Gaur P.M., Varshney R.K. (2018). Super Annigeri
1 and improved JG 74: two Fusarium wilt-resistant in-
trogression lines developed using marker-assisted back-
crossing approach in chickpea (Cicer arietinum L.). Mol.
Breeding, 39: 2.
Martin J.M., Talbert L.E., Lanning S.P., Blake N.K.
(1995). Hybrid performance in wheat as related to pa-
rental diversity. Crop Sci., 35: 104-108.
Melchinger A.E. (1993). Use of RFLP markers for analy-
ses of genetic relationships among breeding materials
and prediction of hybrid performance. p. 621–628. In
D.R. Buxton et al. (ed.) International crop science I.
CSSA, Madison, WI
Melchinger A.E., Utz H.F., Schön C.C. (1998). Quanti-
tative trait locus (QTL) mapping using dierent testers
and independent population samples in maize reveals
low power of QTL detection and large bias in estimates
of QTL eects.Genetics,149: 383–403.
Miklas P.N., Kelly J.D., Beebe S.E., Blair M.W. (2006).
Common bean breeding for resistance against biotic
and abiotic stresses: from classical to MAS breeding.
Euphytica, 147:105-131.
Nachit M.M. (1998). Association of grain yield in dry-
land and carbon isotope discrimination with molecular
markers in durum (Triticum turgidum L. var. durum).
In: Proceedings of 9th International wheat symposium,
Saskatoon, Saskatchewan, Canada, pp. 218-223.
Nachit M.M., Eloua I. (2004). Durum adaptation in
the Mediterranean dryland: breeding, stress physiology
and molecular markers. Challenges and Strategies of
Dryland Agriculture, 32: 203-218. Crop Science Society
of America and American Society of Agronomy, Special
Publication 32: 203-218.
Nakashima K.,NakashimaIto Y., Yamaguchi-Shinozaki
Y. (2009). Transcriptional regulatory networks in re-
sponse to abiotic stresses in Arabidopsis and grasses
Plant Physiology, 149:88-95.
Ni D., Songa F., Nia J., Zhangb A., Wang C., Zhaoc K.,
Yanga Y., Weia P,. Yanga J., Li L. (2015). Marker-assisted
selection of two-line hybrid rice for diseaseresistance
to rice blast and bacterial blight. Field Crops Research,
184: 1–8.
Olson R.K., Wise B., Conners F., Rack J., Fulker D. (1989).
Specic decits in component reading and language
skills: Genetic and environmental inuences. Journal of
Learning Disabilities, 22: 339-348.
Omoigui L.O., Kamara A.Y., Moukoumbi Y.D., Ogunk-
anmi L.A., Timko M.P. (2017). Breeding cowpea for
resistance to Striga gesnerioides in the Nigerian dry sa-
vannas using marker-assisted selection. Plant Breeding,
136: 393-399.
Paran I., Michelmore R.W. (1993). Development of reli-
able PCR-based markers linked to downy mildew resis-
tance genes in lettuce. eor. Appl. Genet.,85:985-993.
246 Henkrar and Udupa: Marker assisted selection for plant breeding
Powell WW, Koput K, Smith-Doerr L. (1996). Interor-
ganization collaboration and the locus of innovation:
Networks of learning in biotechnology. Admin. Sci.
Quart., 41: 116–145.
Prasanna H.C., Kashyap S.P., Krishna R., Sinha D.P.,
Reddy S., Malathi V.G. (2015). Marker assisted selection
of Ty-2 and Ty-3 carrying tomato lines and their impli-
cations in breeding tomato leaf curl disease resistant
hybrids. Euphytica, 64: 256–264.
Pukalenthy B., Manickam D., Chandran S., Adhimoolam
K., Sampathrajan V., Rajasekaran R., Arunachalam K.,
Ganapathyswamy H., Chocklingam V., Muthusamy V.,
Hossain F., Natesan S. (2019). Incorporation of opaque-2
into ‘UMI 1200’, an elite maize inbred line, through
marker-assisted backcross breeding. Biotechnology and
Biotechnological Equipment, 33: 144-153.
Randhawa H.S., Asif M., Pozniak C.J., Clarke J.M., Graf
R.J., Fox S.L., Humphreys D.G., Knox R.E., DePauw
R.M., Singh A.K., Cuthbert R.D., Hucl P.J., Spaner D.M.
(2013). Application of molecular markers to wheat
breeding in Canada. Plant Breeding, 132: 458-471.
Ribaut J.M., Ragot M. (2007). Marker-assisted selection
to improve drought adaptation in maize: the backcross
approach, perspectives, limitations, and alternatives.J.
Exp. Bot., 58: 351–360.
Ryan J., Ibrahim H., Dakermanji A., Niane A.A. (2012).
Training and Capacity Building: An Essential Strategy
for Development at an International Research Center.
Sustainable Agriculture Research, 1.
Sax K. (1923). e association of size dierences with
seed-coat pattern and pigmentation inPhaseolus Vul-
garis.Genetics,8:552–560.
Sayed H., Baum M. (2018). Marker-assisted selection for
scald (Rhynchosporium commune L.) resistance gene(s)
in barley breeding for dry areas. Journal of Plant Protec-
tion Research, 4: 335–344.
Shim J., Torollo G., Angeles-Shim R.B., Cabunagan
R.C., Choi I.R., Yeo U.S., Ha W.G. (2015). Rice tungro
spherical virus resistance into photoperiod-insensitive
japonica rice by marker-assisted selection.Breed. Sci.,
65:345-51.
Singh I., Smita S., Mishra D.C., Kumar S., Singh B.K.,
Rai A. (2017). Abiotic Stress Responsive miRNA-Target
Network and Related Markers (SNP, SSR) inBrassica
juncea.Front. Plant Sci.,8: 1943.
Singh M., Mallick N., Chand S., Kumari P., Sharma
J.B., Sivasamy M., Jayaprakash P., Prabhu K.V., Jha S.K.
(2017). Marker-assisted pyramiding of inopyrum-
derived leaf rust resistance genes Lr19 and Lr24 in bread
wheat variety HD2733. J. Genet., 96:951–957.
Sourour A., Othmani A., Bechrif S., Rezgui M., Ben
Younes M. (2018). Correlation between agronomical
and quality traits in durum wheat (Triticum durum
Desf.) germplasm in semi arid environment. Advances
in Plants and Agriculture Research, 8:612‒615
Steele J.G., Sanders A.E., Slade G.D., Allen P.F., Lahti S.,
Nuttall N., Spencer A.J. (2004). How do age and tooth
loss aect oral health impacts and quality of life? A study
comparing two national samples. Community Dent. Oral
Epidemiol., 32:107-14.
Steele K.A., Price A.H., Shashidhar H.E., Witcombe J.R.
(2006). Marker-assisted selection to introgress rice QTLs
controlling root traits into an Indian upland rice variety.
eor. Appl. Genet., 112: 208–222.
Tadesse W., Abdalla O., Ogbonnaya F., Nazari K., Tahir
I., Baum M. (2012). Agronomic performance of elite
stem rust resistant spring wheat genotypes and associa-
tion among trial sites in the CWANA region. Crop Sci.,
52: 1105-1114.
Tadesse W., Ogbonnaya F.C., Jighly A., Nazari K., Raja-
ram S., Baum M. (2014). Association Mapping of Resis-
tance to Yellow Rust in Winter Wheat Cultivars and Elite
Genotypes. Crop Sci., 54: 607–616.
TadesseW.,NachitM., AbdallaO.,RajaramS. (2016).
“Wheat breeding at ICARDA: Achievements and pros-
pects in the CWANA region, inAlain,B.,Bill,A.andM
aarten van,G.(Eds),e World Wheat Book Volume 3:
A History of Wheat Breeding,Lavoisier,Paris,I
Tanaka Y., Yoneda H., Hosokawa M., Miwa T., Yazawa
S. (2014). Application of marker-assisted selection in
breeding of a new fresh pepper cultivar (Capsicum an-
nuum) containing capsinoids, low-pungent capsaicinoid
analogs. Sci. Hortic., 165: 242–245.
Tanksley S.D., Young N.D., Paterson A.H., Bonierbale
M.W. (1989). RFLP mapping in plant breeding: new tools
for an old science. BioTechnology, 7: 257-264.
oday J.M. (1961). Location of polygenes. Nature, 191:
368-370.
Todaka D., Nakashima K., Maruyama K., Kidokoro S.,
Osakabe Y., Ito Y., Matsukura S., Fujita Y., Yoshiwara K.,
Ohme-Takagi M., Kojima M., Sakakibara H., Shinozaki
K., Yamaguchi-Shinozaki K. (2012).Rice phytochrome-
interacting factor-like protein OsPIL1 functions as a key
regulator of internode elongation and induces a mor-
phological response to drought stress.Proc. Natl. Acad.
Sci. U.S.A,109:15947–15952.
Vishwakarma M.K., Mishra V.K., Gupta P.K., Yadav P.S.,
Kumar H., Joshi A.K. (2016). Introgression of the high
grain protein gene Gpc-B1 in an elite wheat variety of
Indo-Gangetic Plains through marker assisted backcross
breeding. Curr. Plant Biol., 1: 60–67.
Visioni A., Gyawali S., Selvakumar R., Gangwar O.P.,
Shekhawat P.S., Bhardwaj S.C., Al-Abdallat A.M., Kehel
Z., Verma R.P.S. (2018). Genome Wide Association
Mapping of Seedling and Adult Plant Resistance to
Barley Stripe Rust (Puccinia striiformisf. sp.hordei) in
India.Front. Plant Sci., 9: 520.
Vos P., Hogers R., Bleeker M., Reijans M., van de Lee
T., Hornes M., Freijters A., Pot J., Peleman J., Kuiper
M., Zabeau M. (1995). AFLP: a new concept for DNA
ngerprinting. Nucleic Acids Res., 21: 4407-4414.
Wang D, Zhao L, Li K, Ma Y, Wang L, Yang Y, Yang Y,
Zhi H (2017). Marker-assisted pyramiding of soybean
resistance genes RSC4, RSC8, and RSC14Q to soybean
mosaic virus. J. Integr. Agr., 16: 2413–2420.
Welcker C., Boussuge B., Benciveni C., Ribaut J.M., Tar-
dieu F. (2007). Are source and sink strengths genetically
linked in maize plants subjected to water decit? A QTL
study of the responses of leaf growth and anthesis-silking
interval to water decit.J. Exp. Bot.,58:339–349.
247
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
Wilde F., Schön C.C., Korzun V., Ebmeyer E., Schmolke
M., Hartl L., Miedaner T. (2008). eor. Appl. Genet.,
117: 29.
Williams J.G.K., Kubelik A.R., Livak K.J., Rafalski J.A.,
Tingey S.V. (1990). DNA polymorphisms amplied by
arbitrary primers are useful as genetic markers. Nucl.
Acids Res., 18: 6531-6535.
Xu Y., Crouch J.H. (2008). Marker-assisted selection in
plant breeding: from publication to practice. Crop Sci.,
48: 391-407.
Xu Y., Zhang X.Q., Harasymow S., Westcott S., Zhang
W., Li C. (2018). Molecular marker-assisted backcross-
ing breeding: an example to transfer a thermostable
β-amylase gene from wild barley. Mol. Breeding, 38:63.
Yagdi K.S. (2009). Heritability, variance components and
correlations of yield and quality traits in durum wheat
(Triticum durum desf.). Pak. J. Bot., 41:753‒759.
Yamaguchi-Shinozaki K., Shinozaki K. (2006). Tran-
scriptional regulatory networks in cellular responses and
tolerance to dehydration and cold stresses.Annu. Rev.
Plant Biol.,57: 781–803.
Yang D., Tang J., Yang D., Chen Y., Ali J., Mou T. (2019).
Improving rice blast resistance of Feng39S through mo-
lecular marker-assisted backcrossing.Rice (New York,
N.Y.)12: 70.
Young N.D. (1999). A cautiously optimistic vision for
marker-assisted breeding. Mol. Breeding, 5: 505-510.
Yugander A., Sundaram R.M., Singh K., Ladhalakshmi
D., Subba Rao L.V., Madhav M.S., Badri J., Prasad M.S.,
Laha G.S. (2018. Incorporation of the novel bacterial
blight resistance gene Xa38 into the genetic background
of elite rice variety Improved Samba Mahsuri. PLoS
One,13:e0198260.
Zhao Q.Y., Zhu Z., Zhang Y.D., Zhao L., Chen T., Zhang
Q.F., Wang C.L. (2008). Correlation analysis between ge-
netic distance of SSR markers and heterosis japonica. e
Fih National Congress of Plant Molecular Breeding-
cum-academic exchanges Proceedings.
Zhao X., Tan G., Xing Y., Wei L., Chao Q., Zuo W.,
Lübberstedt T., Xu M. (2012). Marker-assisted introgres-
sion of qHSR1 to improve maize resistance to head smut.
Mol. Breeding, 30:1077–1088.
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